MDX Numeric Functions: The .Ordinal Function - Page 5

September 5, 2006

Procedure: Use the .Ordinal Function as a Basis for Conditionally Displaying a Value

Recall that, in addition to the need to support parameters, the client representatives have asked that we craft a query to generate a dataset that shows an already existing cube measure, Internet Sales Amount in one column, with a calculation, "Monthly Moving Avg," (based upon a rolling six months' activity) alongside it. With the levels of the Calendar Year dimension (for 2003 only, in our example) – that is months, quarters, half-years and year - as the "Y" axis, the business need is to show total Internet Sales Amount for each Date dimension level, but to show the rolling average only on rows representing months, as the average is (at least to the management audience) only relevant at the monthly level. Finally, recall that, instead of displaying a blank space or a zero within the rows where Monthly Moving Avg does not appear, the specification expresses a preference for the display to be "N/A."

The report authors / developers express appreciation for the fact that "showing different values at different levels" might be a useful capability in a wide range of (potentially much more sophisticated) scenarios arising within their environment. They listen as we emphasize that the conceptual mechanics to support such conditional presentations might, indeed, be extrapolated well beyond the dimensions and values we are currently using, as well as beyond the use of the .Ordinal function itself within this context.

Having obtained consensus on the proposed target dataset, we are ready to set about constructing the query.

Another new tab again appears, with a connection to the Adventure Works cube, in the Query pane.

--MDX047-003 Conditional presentation of a calculation, based upon an 
-- existing measure and the MDX .Ordinal function
WITH
MEMBER
   [MEASURES].[Monthly Moving Avg]
AS
 'IIF( 
  
      [Date].[Calendar].CURRENTMEMBER.LEVEL.ORDINAL = 
         [Date].[Calendar].[Month].ORDINAL,
  
             AVG(LASTPERIODS (6, [Date].[Calendar].CURRENTMEMBER), 
         [Measures].[Internet Sales Amount]), 
   
      NULL)', 
   FORMAT_STRING = "$#,##0;0;0;\N\\\A\"
  
SELECT
 {[Measures].[Internet Sales Amount], [MEASURES].[Monthly Moving Avg]} 
      ON AXIS (0),
 
 {DESCENDANTS([Date].[Calendar].[Calendar Year].&[2003],
      [Date].[Calendar].[Month], SELF_AND_BEFORE )}
          
      ON AXIS(1)
             
 FROM
 
 [Adventure Works]

The Query pane appears, with our input, as shown in Illustration 13.

The Results pane is, once again, populated by Analysis Services. This time, the dataset depicted in Illustration 14 appears.

In the returned dataset, we see that the moving average appears only at the monthly levels, thanks to the comparison we perform through the use of the .Ordinal function. We suggest to our client colleagues that the year might be parameterized, that we might build in the capability to switch from Calendar to Fiscal Year, and that we might add myriad other capabilities within the ultimate reporting dataset query. Suffice it to say that, assuming a thorough knowledge of the various layers of the Microsoft integrated BI solution, one can obtain many powerful capabilities and features, and knowing "where to put the intelligence" within the sometimes multiple choices can mean highly tuned performance and effective solutions for consumers throughout our organizations. For more of my observations on this subject see Multi-Layered Business Solutions ... Require Multi-Layered Architects.

The client representatives inform us that their immediate goals have been met, and that the examples we have shared illustrate the principles of operation behind .Ordinal, as a part of helping them to support the expressed business requirements.

Summary ...

In this article, we explored the MDX .Ordinal function, which can be called upon in activities that range from generating simple lists and supporting parameter picklists to constructing a basis upon which we can drive conditional generation and display of calculations and other values. We introduced the function, commenting upon its operation and touching upon the creative effects we can deliver through its use.

We examined the syntax involved with .Ordinal, and then undertook illustrative practice examples of business uses for the function, generating queries that capitalized upon its capabilities. Throughout our practice session, we briefly discussed the results datasets we obtained from each of the queries we constructed, as well as extending our discussion to other possible options and uses for the concepts we exposed.

» See All Articles by Columnist William E. Pearson, III

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